Currently available fitness monitoring devices monitor and track a user's fitness level, for example, by counting the user's steps, total calories burned, miles run, etc., and by monitoring the user's heart rate. Currently available solutions also may seek to determine a proper training load for a user based on universal statistics regarding the user's physical and/or biological characteristics, where such universal statistics attempt to gauge the user's likely response to a given training load. Nevertheless, currently available fitness monitoring devices do not provide modeling performance capabilities or capacities that enable specific prediction of a user's response to activity, rest, and other scenarios, and use the response to determine a training load based on the user's performance capacity reflected by the predicted response. Rather, current solutions are limited to merely tracking a user's activity and response to the same, and thus do not provide training loads that holistically maximize the user's performance capacity in a balanced way, nor do they provide the ability to meaningfully compare different potential training loads, hence do not provide the user with the user's best overall fitness and well-being.